spark-bench is an open-source benchmarking tool, and it’s also so much more. spark-bench is a flexible system for simulating, comparing, testing, and benchmarking Spark applications and Spark itself. spark-bench originally began as a benchmarking suite to get timing numbers on very specific algorithms mostly in the machine learning domain. Since then it has morphed into a highly configurable and flexible framework suitable for many use cases.

This talk will discuss the high level design and capabilities of spark-bench before walking through some major, practical use cases. Use cases include, but are certainly not limited to: regression testing changes to Spark; comparing performance of different hardware and Spark tuning options; simulating multiple notebook users hitting a cluster at the same time; comparing parameters of a machine learning algorithm on the same set of data; providing insight into bottlenecks through use of compute-intensive and i/o-intensive workloads; and, yes, even benchmarking. In particular, this talk will address the use of spark-bench in developing new features for Spark core.

Emily is a Software Engineer at The Weather Company (now IBM) working on the data engineering platform team. She lives in her hometown of Atlanta, GA with her husband where she can often be found on the Chattahoochee river in a kayak.

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The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.